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main.py
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main.py
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from __future__ import print_function
import argparse
import json
import numpy as np
import os
import pandas as pd
import random
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from glob import glob
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
from torchvision import models, transforms
from datasets import ImageDataset
from evaluation import Evaluation
from layers import L2Normalization
from losses import HistogramLoss
from samplers import MarketSampler
from visualizer import Visualizer
with open('config') as json_file:
opt = json.load(json_file)
print(opt)
try:
os.makedirs(opt['checkpoints_path'])
except OSError:
pass
if opt['manual_seed'] is None:
opt['manual_seed'] = random.randint(1, 10000)
print("Random Seed: ", opt['manual_seed'])
random.seed(opt['manual_seed'])
torch.manual_seed(opt['manual_seed'])
if opt['cuda']:
torch.cuda.manual_seed_all(opt['manual_seed'])
if torch.cuda.is_available() and not opt['cuda']:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
vis = Visualizer(opt['checkpoints_path'], opt['visdom_port'])
def create_df(dataroot, size=-1):
df_paths = glob(dataroot)
df = pd.DataFrame({'path': df_paths})
df['label'] = df.path.apply(lambda x: int(x.split('/')[-1].split('_')[0]))
return df[:size]
if not opt['market']:
df_train = create_df(os.path.join(opt['dataroot'], '*.jpg'))
else:
def create_market_df(x):
df = create_df(os.path.join(opt['dataroot'], paths[x]))
df['camera'] = df.path.apply(lambda x: int(x.split('/')[-1].split('_')[1].split('s')[0].split('c')[1]))
df['name'] = df.path.apply(lambda x: x.split('/')[-1])
return df
paths = {
'train': 'bounding_box_train/*.jpg',
'test': 'bounding_box_test/*.jpg',
'query': 'query/*.jpg',
}
df_train = create_market_df('train')
dfs_test = {
x: create_market_df(x) for x in ['test', 'query']
}
data_transform_test = transforms.Compose([
transforms.Resize([256, 256]),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
datasets_test = {
x: ImageDataset(
dfs_test[x]['path'],
transform=data_transform_test,
is_train=False
) for x in ['test', 'query']
}
dataloaders_test = {
x: DataLoader(
datasets_test[x],
batch_size=opt['batch_size_test'],
shuffle=False,
num_workers=opt['nworkers']
) for x in datasets_test.keys()
}
evaluation = Evaluation(dfs_test['test'], dfs_test['query'], dataloaders_test['test'], dataloaders_test['query'], opt['cuda'])
data_transform = transforms.Compose([
transforms.Resize([256, 256]),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dataset = ImageDataset(df_train['path'], data_transform, True, df_train['label'])
sampler = MarketSampler(df_train['label'], opt['batch_size'])
dataloader = DataLoader(dataset, batch_sampler=sampler, num_workers=opt['nworkers'])
def train(optimizer, criterion, scheduler, epoch_start, epoch_end):
for epoch in range(epoch_start, epoch_end):
scheduler.step()
model.train(True)
running_loss = .0
for data in dataloader:
inputs, labels = data
inputs, labels = inputs.squeeze(), labels.squeeze()
if opt['cuda']:
inputs, labels = inputs.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.data.item()
epoch_loss = running_loss / len(dataloader)
vis.quality('Loss', {'Loss': epoch_loss}, epoch, opt['nepoch'])
if opt['market']:
if epoch % 5 == 0:
model.train(False)
ranks, mAP = ranks, mAP = evaluation.ranks_map(model, 2)
vis.quality('Rank1 and mAP', {'Rank1': ranks[1], 'mAP': mAP}, epoch, opt['nepoch'])
if epoch % 10 == 0:
torch.save(model, '{}/finetuned_histogram_e{}.pt'.format(opt['checkpoints_path'], epoch))
model = models.resnet34(pretrained=True)
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.fc.in_features
model.fc = torch.nn.Sequential()
if opt['dropout_prob'] > 0:
model.fc.add_module('dropout', nn.Dropout(opt['dropout_prob']))
model.fc.add_module('fc', nn.Linear(num_ftrs, 512))
model.fc.add_module('l2normalization', L2Normalization())
if opt['cuda']:
model = model.cuda()
print(model)
criterion = HistogramLoss(num_steps=opt['nbins'], cuda=opt['cuda'])
if opt['nepoch_fc'] > 0:
print('\nTrain fc layer\n')
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=opt['lr_fc'])
scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
train(optimizer, criterion, scheduler, 1, opt['nepoch_fc'] + 1)
print('\nTrain all layers\n')
for param in model.parameters():
param.requires_grad = True
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=opt['lr'])
scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)
train(optimizer, criterion, scheduler, opt['nepoch_fc'] + 1, opt['nepoch'] + 1)